From topological analyses to functional modeling: the case of hippocampus

TL;DR

This paper demonstrates how topological data analyses can be used to understand hippocampal function, providing a unified framework for integrating neural data and insights into spatial learning processes.

Contribution

It introduces a novel functional model of the hippocampus based on topological analyses, linking neural data structure to cognitive functions.

Findings

Topological analyses reveal data structures related to hippocampal processing.

The model integrates multi-timescale spiking information.

Quantifies effects of physiological phenomena on spatial cognition.

Abstract

Topological data analyses are rapidly turning into key tools for quantifying large volumes of neurobiological data, e.g., for organizing the spiking outputs of large neuronal ensembles and thus gaining insights into the information produced by various networks. Below we discuss a case in which several convergent topological analyses not only provide a description of the data structure, but also produce insights into how these data may be processed in the hippocampus---a brain part that plays a key role in learning and memory. The resulting functional model provides a unifying framework for integrating spiking information at different timescales and understanding the course of spatial learning at different levels of spatiotemporal granularity. In particular, the model allows quantifying contributions of various physiological phenomena---brain waves, synaptic strengths, synaptic architectures, etc., into spatial cognition.